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create_character_from_description

Invent a new character from a text description and generate its portrait — the full "make me a character, no photo involved" path in ONE call, instead of gluing together create_character + update_character + generate_character_image yourself.

name: short and unique among YOUR OWN characters (see list_characters()) — how
you'll refer to this character afterwards, e.g. character="detective_marlowe" in
generate_with_face.
description: physical description — build, hair, eyes, clothing, distinguishing
features. Saved on the character's card AND used to generate the portrait, so
write it as concretely as you would any image prompt.
account: Google account (and its project) to create the character under.

The portrait is written to Flow's card exactly ONCE — that slot cannot be
regenerated. Calling this again with the SAME name does not retry it: it fails
fast with a clear message instead of hitting a raw HTTP 500 downstream. Want a
different look? Use a new name.

Returns {"name", "entity_id", "portrait_media_id", "portrait_url", "hint"} once
done. Next step for ANY further image of this character: generate_with_face(
character=name, prompt="...") — never entity_id/character_slot_index, which only
works for that one portrait write and cannot place the character into new scenes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
modelNoNARWHAL
aspectNoIMAGE_ASPECT_RATIO_PORTRAIT
accountYes
descriptionYes
display_nameNo
portrait_promptNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses key behavioral traits beyond the annotations: the portrait slot is written exactly once and cannot be regenerated, calling again with the same name fails fast with a clear message rather than a raw HTTP 500, and the return includes a hint for future use. These details add significant context that the annotations alone do not provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, with the core purpose front-loaded, followed by parameter guidance, behavioral notes, and return/next-step info. It is moderately verbose but each sentence carries useful information, so it earns a high conciseness score without being overly terse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (7 params, no output schema), the description covers the essential aspects: purpose, key parameter semantics, one-time behavior, failure mode, return values, and the correct next step. It misses some parameter details and does not explain the 'hint' field, but it is sufficiently complete for an agent to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate by explaining parameters. It thoroughly explains the three required ones (name, description, account), but omits explanations for model, aspect, display_name, and portrait_prompt, which have no schema descriptions. The enums for model and aspect are given but their roles are not clarified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: invent a character from a text description and generate its portrait in one call, explicitly contrasting with the alternative of gluing together create_character + update_character + generate_character_image. It uses a specific verb and resource and distinguishes itself from the photo-based sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description tells when to use this tool (the 'no photo involved' path) and explicitly names the alternative multi-call approach, as well as directing future image generation to generate_with_face. It does not explicitly mention create_character_from_photo, but the phrase 'no photo involved' implies that exclusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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